Neural networks are universal function approximators [1]}, which learn a mapping between an input and the output through a series of hidden layers. The latent representation learned in these hidden layers directly controls how each function is realised. We hypothesise that learning clear discrimination between classes in the latent space of object detectors could have two fold effect. First, it helps the model to identify how the feature representation of an unknown instance is different from the other known instances, which helps identify an unknown instance as a novelty. Second, it facilitates learning feature representations for the new class instances without overlapping with the previous classes in the latent space, which helps towards incrementally learning without forgetting.
The key component that helps us realise this is our proposed contrastive clustering in the latent space, which we elaborate in Sec. REF .
